Many business management strategies strive to improve the quality of process outputs by identifying and removing the causes of defects or errors and minimizing variability in the manufacturing and business processes. The strategies often use a set of quality management methods, including statistical methods, and suggest creating a special infrastructure of people within the organization (e.g., “Black Belts,” “Green Belts,” etc.) who are experts in these methods. Projects in the organization may be carried out according to a method and follow a defined sequence of steps toward a specified goal (e.g., a quantified manufacturing tolerance).
Although application of statistical methods to manufacturing and business processes is known to be an effective way to achieve improvements, learning and developing appropriate skills in the application of statistical methods requires patience and diligence. Because manual and human-based decisions are the centerpiece of today's art, proficiency in application of statistical methods for manufacturing and business requires not only an understanding of statistical concepts, methodologies, and calculations but mastery of the statistical analysis software packages that are used for calculation of statistics. For many individuals, mastery of statistics fundamentals is further complicated by the required mastery of the software packages. One of the reasons statistical software packages are difficult even for organizational experts to master is that the interfaces are cumbersome and non-intuitive. They often rely on hundreds of hierarchical menus and submenus that a user must review to set appropriate parameters or to select the appropriate tool. Some packages provide wizards for a few, but not all, of the available tools. Because no wizards are provided for important tools such as nonparametric hypothesis testing and nonlinear regression tests, existing packages are not very useful for manufacturing and business applications. While many statistical software packages are feature-rich and very powerful, they provide far more functionality than is needed for many manufacturing and business applications and therefore, are very difficult to use. In addition, for many unskilled users in statistics, they do not support automated statistical methods that are important for manufacturing and business process statistical analysis. There is a need for a statistical software package directed to mainstream, unskilled users in statistics for manufacturing and business applications with an intuitive, automated and improved user interface. There is a need for a statistical software package with an interface that allows users unskilled in the art of statistics to produce high quality statistical and data analysis with minimal user input.